LyDROO

Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks

PyTorch code to reproduce LyDROO algorithm [1], which is an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints. It applies Lyapunov optimization to decouple the multi-stage stochastic MINLP into deterministic per-frame MINLP subproblems and solves each subproblem via DROO algorithm. It includes:

• memory.py: the codes of Deep Reinforcement Learning

• ResourceAllocation: Algorithms for resource allocation

• LyDROO.py: run this file for LyDROO

About our works

1. Suzhi Bi, Liang Huang, and Ying-jun Angela Zhang, ``Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks'', IEEE Transactions on Wireless Communications, 2021, doi:10.1109/TWC.2021.3085319.

About authors

• Suzhi BI, bsz AT szu.edu.cn

• Liang HUANG, lianghuang AT zjut.edu.cn

• Ying Jun (Angela) Zhang, yjzhang AT ie.cuhk.edu.hk

How the code works

• For LyDROO algorithm, run the file, LyDROO.py